Technical Indicator's of Stock market.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "Technical Indicator's of Indian Stock market.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishkumar30/ML-AI-Python-Codes/blob/master/Technical%20Indicator's%20of%20Stock%20market.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxlpnhbDUaBl"
      },
      "source": [
        "# INDICATORS (ATR,RSI,SMA,EMA,Bollinger band ) on Historical_data 'SBI'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5niK2jvhUaBq"
      },
      "source": [
        "Login to Zerodha and fetching Histrical Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkUeLaxSUaBs"
      },
      "source": [
        "import time\n",
        "import secrets \n",
        "import threading\n",
        "import webbrowser\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "from math import modf\n",
        "from pprint import pprint\n",
        "from pytz import timezone\n",
        "from datetime import datetime \n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from time import gmtime, strftime\n",
        "from kiteconnect import KiteTicker \n",
        "from kiteconnect import KiteConnect\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# API KEY and API secter \n",
        "api_key='******************* @@enter your api key here@@@**************' \n",
        "api_secret='**********************@@enter your api key here****************'  \n",
        "\n",
        "webbrowser.open_new_tab(KiteConnect(api_key,api_secret).login_url())\n",
        "kite = KiteConnect(api_key=api_key)\n",
        "data = kite.generate_session(((str(input(\"ENTER full link generated in URL :- \")).split(\"request_token=\")[1]).split(\"&action\"))[0],api_secret)\n",
        "kite.set_access_token(data[\"access_token\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kVkIxlQFUaB5"
      },
      "source": [
        "#  Getting Historical Data_Download from zerodha for indicator's"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QXDb-hHUaB7",
        "outputId": "744a72ac-92c7-48e5-f2a0-8dc58b280aae"
      },
      "source": [
        "'''\n",
        "    # Date must be present as a Pandas DataFrame with ['date', 'open', 'high', 'low', 'close', 'volume'] as columns\n",
        "    df = pd.DataFrame(data[\"data\"][\"candles\"], columns=['date', 'open', 'high', 'low', 'close', 'volume']) \n",
        "'''\n",
        "df=pd.DataFrame(kite.historical_data(779521,\"2019-04-14 15:00:00\",\"2019-08-16 09:16:00\",\"day\",0))[[\"date\",\"open\",\"high\",\"low\",\"close\",\"volume\"]]\n",
        "display(df.tail(10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>2019-08-01 00:00:00+05:30</td>\n",
              "      <td>330.80</td>\n",
              "      <td>331.50</td>\n",
              "      <td>311.35</td>\n",
              "      <td>317.15</td>\n",
              "      <td>40853304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>2019-08-02 00:00:00+05:30</td>\n",
              "      <td>315.55</td>\n",
              "      <td>322.25</td>\n",
              "      <td>307.05</td>\n",
              "      <td>308.45</td>\n",
              "      <td>64472348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2019-08-05 00:00:00+05:30</td>\n",
              "      <td>298.45</td>\n",
              "      <td>302.85</td>\n",
              "      <td>291.70</td>\n",
              "      <td>300.25</td>\n",
              "      <td>48815899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2019-08-06 00:00:00+05:30</td>\n",
              "      <td>298.80</td>\n",
              "      <td>304.25</td>\n",
              "      <td>297.25</td>\n",
              "      <td>301.40</td>\n",
              "      <td>30442970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>2019-08-07 00:00:00+05:30</td>\n",
              "      <td>302.40</td>\n",
              "      <td>302.45</td>\n",
              "      <td>288.80</td>\n",
              "      <td>289.90</td>\n",
              "      <td>30820114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2019-08-08 00:00:00+05:30</td>\n",
              "      <td>290.00</td>\n",
              "      <td>295.50</td>\n",
              "      <td>285.60</td>\n",
              "      <td>294.35</td>\n",
              "      <td>32929233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2019-08-09 00:00:00+05:30</td>\n",
              "      <td>296.30</td>\n",
              "      <td>298.00</td>\n",
              "      <td>290.05</td>\n",
              "      <td>291.35</td>\n",
              "      <td>23377581</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2019-08-13 00:00:00+05:30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>291.55</td>\n",
              "      <td>282.50</td>\n",
              "      <td>283.35</td>\n",
              "      <td>24231272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2019-08-14 00:00:00+05:30</td>\n",
              "      <td>285.05</td>\n",
              "      <td>291.25</td>\n",
              "      <td>284.50</td>\n",
              "      <td>289.75</td>\n",
              "      <td>18523649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2019-08-16 00:00:00+05:30</td>\n",
              "      <td>287.95</td>\n",
              "      <td>292.80</td>\n",
              "      <td>284.30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>20047399</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        date    open    high     low   close    volume\n",
              "73 2019-08-01 00:00:00+05:30  330.80  331.50  311.35  317.15  40853304\n",
              "74 2019-08-02 00:00:00+05:30  315.55  322.25  307.05  308.45  64472348\n",
              "75 2019-08-05 00:00:00+05:30  298.45  302.85  291.70  300.25  48815899\n",
              "76 2019-08-06 00:00:00+05:30  298.80  304.25  297.25  301.40  30442970\n",
              "77 2019-08-07 00:00:00+05:30  302.40  302.45  288.80  289.90  30820114\n",
              "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233\n",
              "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581\n",
              "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272\n",
              "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649\n",
              "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L72FGwB0UaCD"
      },
      "source": [
        "# Ploting data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCUXtnGrUaCG",
        "outputId": "4bfadf37-5b7c-4df9-9584-fdb999eed60b"
      },
      "source": [
        "plt.style.use('seaborn')\n",
        "df['high'].plot()\n",
        "df['low'].plot()\n",
        "df['close'].plot()\n",
        "df['open'].plot()\n",
        "plt.show()\n",
        "print (\"  \\t'---- ABOVE GRAPH OF OHLC Historical data ----- ' \\n \\n \")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "  \t'---- ABOVE GRAPH OF OHLC Historical data ----- ' \n",
            " \n",
            " \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OEah9sm2UaCL"
      },
      "source": [
        "# Indicator's"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHsfpWtaUaCN"
      },
      "source": [
        "# 1. ATR (Average True Range)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2EXMp-RJUaCP",
        "outputId": "623e74fe-110b-488a-efe7-b22fa42c783d"
      },
      "source": [
        "\"\"\"\n",
        "Function to compute Average True Range (ATR)\n",
        "\n",
        "Args :\n",
        "    df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "    period : Integer indicates the period of computation in terms of number of candles\n",
        "    ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
        "\n",
        "Returns :\n",
        "    df : Pandas DataFrame with new columns added for \n",
        "        True Range (TR)\n",
        "        ATR (ATR_$period)\n",
        "\"\"\"\n",
        "\n",
        "def atr(df,period):\n",
        "    df['hl']=abs(df['high']-df['low'])\n",
        "    df['hpc']=abs(df['high']-df['close'].shift())\n",
        "    df['lpc']=abs(df['low']-df['close'].shift())\n",
        "    df['tr']=df[['hl','hpc','lpc']].max(axis=1)\n",
        "    df['ATR']=pd.DataFrame.ewm(df[\"tr\"], span=period,min_periods=period).mean()\n",
        "    df.drop([\"hl\",\"hpc\",\"lpc\",\"tr\"],axis = 1 , inplace =True)\n",
        "atr(df,14)\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>ATR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2019-08-08 00:00:00+05:30</td>\n",
              "      <td>290.00</td>\n",
              "      <td>295.50</td>\n",
              "      <td>285.60</td>\n",
              "      <td>294.35</td>\n",
              "      <td>32929233</td>\n",
              "      <td>11.851230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2019-08-09 00:00:00+05:30</td>\n",
              "      <td>296.30</td>\n",
              "      <td>298.00</td>\n",
              "      <td>290.05</td>\n",
              "      <td>291.35</td>\n",
              "      <td>23377581</td>\n",
              "      <td>11.331060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2019-08-13 00:00:00+05:30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>291.55</td>\n",
              "      <td>282.50</td>\n",
              "      <td>283.35</td>\n",
              "      <td>24231272</td>\n",
              "      <td>11.026916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2019-08-14 00:00:00+05:30</td>\n",
              "      <td>285.05</td>\n",
              "      <td>291.25</td>\n",
              "      <td>284.50</td>\n",
              "      <td>289.75</td>\n",
              "      <td>18523649</td>\n",
              "      <td>10.609991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2019-08-16 00:00:00+05:30</td>\n",
              "      <td>287.95</td>\n",
              "      <td>292.80</td>\n",
              "      <td>284.30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>20047399</td>\n",
              "      <td>10.328657</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        date    open    high     low   close    volume  \\\n",
              "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
              "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
              "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
              "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
              "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
              "\n",
              "          ATR  \n",
              "78  11.851230  \n",
              "79  11.331060  \n",
              "80  11.026916  \n",
              "81  10.609991  \n",
              "82  10.328657  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X68MhhvyUaCV"
      },
      "source": [
        "# 2. RSI (Relative Strength Index)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBMv3cfGUaCX",
        "outputId": "7a7335f2-b988-4dd2-a0a9-e6c5a3d05ba4"
      },
      "source": [
        "\"\"\"\n",
        "    Function to compute Relative Strength Index (RSI)\n",
        "    \n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        period : Integer indicates the period of computation in terms of number of candles\n",
        "        \n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new columns added for \n",
        "            Relative Strength Index (RSI_$period)\n",
        "    \"\"\"\n",
        "\n",
        "rsi_period=14\n",
        "chg=df[\"close\"].diff(1)\n",
        "gain=chg.mask(chg<0,0) \n",
        "loss=chg.mask(chg>0,0)\n",
        "avg_gain=gain.ewm(com=rsi_period-1,min_periods=rsi_period).mean()\n",
        "avg_loss=loss.ewm(com=rsi_period-1,min_periods=rsi_period).mean()\n",
        "rs =abs(avg_gain / avg_loss)\n",
        "rsi =100 -(100/(1+rs))\n",
        "df['rsi']=rsi\n",
        "display(df.tail())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>ATR</th>\n",
              "      <th>rsi</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2019-08-08 00:00:00+05:30</td>\n",
              "      <td>290.00</td>\n",
              "      <td>295.50</td>\n",
              "      <td>285.60</td>\n",
              "      <td>294.35</td>\n",
              "      <td>32929233</td>\n",
              "      <td>11.851230</td>\n",
              "      <td>26.529639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2019-08-09 00:00:00+05:30</td>\n",
              "      <td>296.30</td>\n",
              "      <td>298.00</td>\n",
              "      <td>290.05</td>\n",
              "      <td>291.35</td>\n",
              "      <td>23377581</td>\n",
              "      <td>11.331060</td>\n",
              "      <td>25.567214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2019-08-13 00:00:00+05:30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>291.55</td>\n",
              "      <td>282.50</td>\n",
              "      <td>283.35</td>\n",
              "      <td>24231272</td>\n",
              "      <td>11.026916</td>\n",
              "      <td>23.154909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2019-08-14 00:00:00+05:30</td>\n",
              "      <td>285.05</td>\n",
              "      <td>291.25</td>\n",
              "      <td>284.50</td>\n",
              "      <td>289.75</td>\n",
              "      <td>18523649</td>\n",
              "      <td>10.609991</td>\n",
              "      <td>28.931857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2019-08-16 00:00:00+05:30</td>\n",
              "      <td>287.95</td>\n",
              "      <td>292.80</td>\n",
              "      <td>284.30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>20047399</td>\n",
              "      <td>10.328657</td>\n",
              "      <td>29.950888</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        date    open    high     low   close    volume  \\\n",
              "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
              "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
              "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
              "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
              "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
              "\n",
              "          ATR        rsi  \n",
              "78  11.851230  26.529639  \n",
              "79  11.331060  25.567214  \n",
              "80  11.026916  23.154909  \n",
              "81  10.609991  28.931857  \n",
              "82  10.328657  29.950888  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dt7ogL5qUaCc"
      },
      "source": [
        "# 3. SMA (simple moving average)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeaRe85xUaCd",
        "outputId": "16141ec2-25a3-44fe-f63c-88afb9c594e5"
      },
      "source": [
        "\"\"\"\n",
        "    Function to compute Simple Moving Average (SMA)\n",
        "    \n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        base : String indicating the column name from which the SMA needs to be computed from\n",
        "        target : String indicates the column name to which the computed data needs to be stored\n",
        "        period : Integer indicates the period of computation in terms of number of candles\n",
        "        \n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new column added with name 'target'\n",
        "    \"\"\"\n",
        "\n",
        "def SMA(df, column=\"close\", period=14):\n",
        "    smavg = df.close.rolling(14).mean()\n",
        "    return df.join(smavg.to_frame('SMA'))\n",
        "a=SMA(df)\n",
        "a.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>ATR</th>\n",
              "      <th>rsi</th>\n",
              "      <th>SMA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2019-08-08 00:00:00+05:30</td>\n",
              "      <td>290.00</td>\n",
              "      <td>295.50</td>\n",
              "      <td>285.60</td>\n",
              "      <td>294.35</td>\n",
              "      <td>32929233</td>\n",
              "      <td>11.851230</td>\n",
              "      <td>26.529639</td>\n",
              "      <td>323.685714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2019-08-09 00:00:00+05:30</td>\n",
              "      <td>296.30</td>\n",
              "      <td>298.00</td>\n",
              "      <td>290.05</td>\n",
              "      <td>291.35</td>\n",
              "      <td>23377581</td>\n",
              "      <td>11.331060</td>\n",
              "      <td>25.567214</td>\n",
              "      <td>319.435714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2019-08-13 00:00:00+05:30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>291.55</td>\n",
              "      <td>282.50</td>\n",
              "      <td>283.35</td>\n",
              "      <td>24231272</td>\n",
              "      <td>11.026916</td>\n",
              "      <td>23.154909</td>\n",
              "      <td>315.232143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2019-08-14 00:00:00+05:30</td>\n",
              "      <td>285.05</td>\n",
              "      <td>291.25</td>\n",
              "      <td>284.50</td>\n",
              "      <td>289.75</td>\n",
              "      <td>18523649</td>\n",
              "      <td>10.609991</td>\n",
              "      <td>28.931857</td>\n",
              "      <td>311.671429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2019-08-16 00:00:00+05:30</td>\n",
              "      <td>287.95</td>\n",
              "      <td>292.80</td>\n",
              "      <td>284.30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>20047399</td>\n",
              "      <td>10.328657</td>\n",
              "      <td>29.950888</td>\n",
              "      <td>308.071429</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        date    open    high     low   close    volume  \\\n",
              "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
              "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
              "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
              "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
              "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
              "\n",
              "          ATR        rsi         SMA  \n",
              "78  11.851230  26.529639  323.685714  \n",
              "79  11.331060  25.567214  319.435714  \n",
              "80  11.026916  23.154909  315.232143  \n",
              "81  10.609991  28.931857  311.671429  \n",
              "82  10.328657  29.950888  308.071429  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y2QQpPm-UaCi"
      },
      "source": [
        "# 4. EMA ( Exponential Moving Average)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbZg7D4aUaCk",
        "outputId": "f3b55f05-a145-4a11-bd07-47261398df4e"
      },
      "source": [
        "\"\"\"\n",
        "    Function to compute Exponential Moving Average (EMA)\n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        base : String indicating the column name from which the EMA needs to be computed from\n",
        "        target : String indicates the column name to which the computed data needs to be stored\n",
        "        period : Integer indicates the period of computation in terms of number of candles\n",
        "        alpha : Boolean if True indicates to use the formula for computing EMA using alpha (default is False)\n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new column added with name 'target'\n",
        "    \"\"\"\n",
        "def EMA(df, column=\"close\", period=14):\n",
        "    ema = df[column].ewm(span=period, min_periods=period - 1).mean()\n",
        "    return df.join(ema.to_frame('EMA'))\n",
        "pp= ( EMA(df)[\"EMA\"].shift(1)*13 + df['close'] ) /  14\n",
        "pp.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "78    321.111774\n",
              "79    317.329208\n",
              "80    313.293848\n",
              "81    309.758441\n",
              "82    307.172770\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLkKjtgCUaCq"
      },
      "source": [
        "# 5. Bollinger band"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4omzQBgEUaCs",
        "outputId": "1a635bcf-c4cf-42f0-b4a0-c32388c89612"
      },
      "source": [
        "def BollingerBand(df, column=\"close\", period=20):\n",
        "    sma = df[column].rolling(window=period, min_periods=period - 1).mean()\n",
        "    std = df[column].rolling(window=period, min_periods=period - 1).std()\n",
        "    up = (sma + (std * 2)).to_frame('BBANDUP')\n",
        "    lower = (sma - (std * 2)).to_frame('BBANDLO')\n",
        "    return df.join(up).join(lower)\n",
        "c=BollingerBand(df)\n",
        "c.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>ATR</th>\n",
              "      <th>rsi</th>\n",
              "      <th>BBANDUP</th>\n",
              "      <th>BBANDLO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2019-08-08 00:00:00+05:30</td>\n",
              "      <td>290.00</td>\n",
              "      <td>295.50</td>\n",
              "      <td>285.60</td>\n",
              "      <td>294.35</td>\n",
              "      <td>32929233</td>\n",
              "      <td>11.851230</td>\n",
              "      <td>26.529639</td>\n",
              "      <td>387.024069</td>\n",
              "      <td>284.140931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>2019-08-09 00:00:00+05:30</td>\n",
              "      <td>296.30</td>\n",
              "      <td>298.00</td>\n",
              "      <td>290.05</td>\n",
              "      <td>291.35</td>\n",
              "      <td>23377581</td>\n",
              "      <td>11.331060</td>\n",
              "      <td>25.567214</td>\n",
              "      <td>385.242185</td>\n",
              "      <td>278.697815</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>2019-08-13 00:00:00+05:30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>291.55</td>\n",
              "      <td>282.50</td>\n",
              "      <td>283.35</td>\n",
              "      <td>24231272</td>\n",
              "      <td>11.026916</td>\n",
              "      <td>23.154909</td>\n",
              "      <td>383.881457</td>\n",
              "      <td>272.388543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>2019-08-14 00:00:00+05:30</td>\n",
              "      <td>285.05</td>\n",
              "      <td>291.25</td>\n",
              "      <td>284.50</td>\n",
              "      <td>289.75</td>\n",
              "      <td>18523649</td>\n",
              "      <td>10.609991</td>\n",
              "      <td>28.931857</td>\n",
              "      <td>379.931275</td>\n",
              "      <td>268.878725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>2019-08-16 00:00:00+05:30</td>\n",
              "      <td>287.95</td>\n",
              "      <td>292.80</td>\n",
              "      <td>284.30</td>\n",
              "      <td>290.90</td>\n",
              "      <td>20047399</td>\n",
              "      <td>10.328657</td>\n",
              "      <td>29.950888</td>\n",
              "      <td>372.909758</td>\n",
              "      <td>267.750242</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        date    open    high     low   close    volume  \\\n",
              "78 2019-08-08 00:00:00+05:30  290.00  295.50  285.60  294.35  32929233   \n",
              "79 2019-08-09 00:00:00+05:30  296.30  298.00  290.05  291.35  23377581   \n",
              "80 2019-08-13 00:00:00+05:30  290.90  291.55  282.50  283.35  24231272   \n",
              "81 2019-08-14 00:00:00+05:30  285.05  291.25  284.50  289.75  18523649   \n",
              "82 2019-08-16 00:00:00+05:30  287.95  292.80  284.30  290.90  20047399   \n",
              "\n",
              "          ATR        rsi     BBANDUP     BBANDLO  \n",
              "78  11.851230  26.529639  387.024069  284.140931  \n",
              "79  11.331060  25.567214  385.242185  278.697815  \n",
              "80  11.026916  23.154909  383.881457  272.388543  \n",
              "81  10.609991  28.931857  379.931275  268.878725  \n",
              "82  10.328657  29.950888  372.909758  267.750242  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OXdUBRz6UaCw"
      },
      "source": [
        "* * * * * "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1aIeNajLUaCy"
      },
      "source": [
        "# Other indicator's hint  (HA,MACD, STDDEV, SuperTrend, Ichimoku)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GInDp_poUaCz"
      },
      "source": [
        "# 6. HA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6Ai7dQCUaC0"
      },
      "source": [
        "\n",
        "def HA(df, ohlc=['Open', 'High', 'Low', 'Close']):\n",
        "    \"\"\"\n",
        "    Function to compute Heiken Ashi Candles (HA)\n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new columns added for \n",
        "            Heiken Ashi Close (HA_$ohlc[3])\n",
        "            Heiken Ashi Open (HA_$ohlc[0])\n",
        "            Heiken Ashi High (HA_$ohlc[1])\n",
        "            Heiken Ashi Low (HA_$ohlc[2])\n",
        "    \"\"\"\n",
        "    ha_open = 'HA_' + ohlc[0]\n",
        "    ha_high = 'HA_' + ohlc[1]\n",
        "    ha_low = 'HA_' + ohlc[2]\n",
        "    ha_close = 'HA_' + ohlc[3]\n",
        "    df[ha_close] = (df[ohlc[0]] + df[ohlc[1]] + df[ohlc[2]] + df[ohlc[3]]) / 4\n",
        "    df[ha_open] = 0.00\n",
        "    for i in range(0, len(df)):\n",
        "        if i == 0:\n",
        "            df[ha_open].iat[i] = (df[ohlc[0]].iat[i] + df[ohlc[3]].iat[i]) / 2\n",
        "        else:\n",
        "            df[ha_open].iat[i] = (df[ha_open].iat[i - 1] + df[ha_close].iat[i - 1]) / 2\n",
        "    df[ha_high]=df[[ha_open, ha_close, ohlc[1]]].max(axis=1)\n",
        "    df[ha_low]=df[[ha_open, ha_close, ohlc[2]]].min(axis=1)\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bsiGI3lNUaC6"
      },
      "source": [
        "# 7. STDDEV"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPRvYlfjUaC7"
      },
      "source": [
        "def STDDEV(df, base, target, period):\n",
        "    \"\"\"\n",
        "    Function to compute Standard Deviation (STDDEV)\n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        base : String indicating the column name from which the SMA needs to be computed from\n",
        "        target : String indicates the column name to which the computed data needs to be stored\n",
        "        period : Integer indicates the period of computation in terms of number of candles\n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new column added with name 'target'\n",
        "    \"\"\"\n",
        "    df[target] = df[base].rolling(window=period).std()\n",
        "    df[target].fillna(0, inplace=True)\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcdXS9UKUaDA"
      },
      "source": [
        "# 8. SuperTrend"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RvaP8DZeUaDC"
      },
      "source": [
        "def SuperTrend(df, period, multiplier, ohlc=['Open', 'High', 'Low', 'Close']):\n",
        "    \"\"\"\n",
        "    Function to compute SuperTrend\n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        period : Integer indicates the period of computation in terms of number of candles\n",
        "        multiplier : Integer indicates value to multiply the ATR\n",
        "        ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new columns added for \n",
        "            True Range (TR), ATR (ATR_$period)\n",
        "            SuperTrend (ST_$period_$multiplier)\n",
        "            SuperTrend Direction (STX_$period_$multiplier)\n",
        "    \"\"\"\n",
        "    ATR(df, period, ohlc=ohlc)\n",
        "    atr = 'ATR_' + str(period)\n",
        "    st = 'ST_' + str(period) + '_' + str(multiplier)\n",
        "    stx = 'STX_' + str(period) + '_' + str(multiplier)\n",
        "    \"\"\"\n",
        "    SuperTrend Algorithm :\n",
        "        BASIC UPPERBAND = (HIGH + LOW) / 2 + Multiplier * ATR\n",
        "        BASIC LOWERBAND = (HIGH + LOW) / 2 - Multiplier * ATR\n",
        "        FINAL UPPERBAND = IF( (Current BASICUPPERBAND < Previous FINAL UPPERBAND) or (Previous Close > Previous FINAL UPPERBAND))\n",
        "                            THEN (Current BASIC UPPERBAND) ELSE Previous FINALUPPERBAND)\n",
        "        FINAL LOWERBAND = IF( (Current BASIC LOWERBAND > Previous FINAL LOWERBAND) or (Previous Close < Previous FINAL LOWERBAND)) \n",
        "                            THEN (Current BASIC LOWERBAND) ELSE Previous FINAL LOWERBAND)\n",
        "        SUPERTREND = IF((Previous SUPERTREND = Previous FINAL UPPERBAND) and (Current Close <= Current FINAL UPPERBAND)) THEN\n",
        "                        Current FINAL UPPERBAND\n",
        "                    ELSE\n",
        "                        IF((Previous SUPERTREND = Previous FINAL UPPERBAND) and (Current Close > Current FINAL UPPERBAND)) THEN\n",
        "                            Current FINAL LOWERBAND\n",
        "                        ELSE\n",
        "                            IF((Previous SUPERTREND = Previous FINAL LOWERBAND) and (Current Close >= Current FINAL LOWERBAND)) THEN\n",
        "                                Current FINAL LOWERBAND\n",
        "                            ELSE\n",
        "                                IF((Previous SUPERTREND = Previous FINAL LOWERBAND) and (Current Close < Current FINAL LOWERBAND)) THEN\n",
        "                                    Current FINAL UPPERBAND\n",
        "    \"\"\"\n",
        "    # Compute basic upper and lower bands\n",
        "    df['basic_ub'] = (df[ohlc[1]] + df[ohlc[2]]) / 2 + multiplier * df[atr]\n",
        "    df['basic_lb'] = (df[ohlc[1]] + df[ohlc[2]]) / 2 - multiplier * df[atr]\n",
        "    # Compute final upper and lower bands\n",
        "    df['final_ub'] = 0.00\n",
        "    df['final_lb'] = 0.00\n",
        "    for i in range(period, len(df)):\n",
        "        df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df[ohlc[3]].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]\n",
        "        df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df[ohlc[3]].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]\n",
        "    # Set the Supertrend value\n",
        "    df[st] = 0.00\n",
        "    for i in range(period, len(df)):\n",
        "        df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df[ohlc[3]].iat[i] <= df['final_ub'].iat[i] else \\\n",
        "                        df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df[ohlc[3]].iat[i] >  df['final_ub'].iat[i] else \\\n",
        "                        df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df[ohlc[3]].iat[i] >= df['final_lb'].iat[i] else \\\n",
        "                        df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df[ohlc[3]].iat[i] <  df['final_lb'].iat[i] else 0.00     \n",
        "    # Mark the trend direction up/down\n",
        "    df[stx] = np.where((df[st] > 0.00), np.where((df[ohlc[3]] < df[st]), 'down',  'up'), np.NaN)\n",
        "    # Remove basic and final bands from the columns\n",
        "    df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)\n",
        "    df.fillna(0, inplace=True)\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffogq1-eUaDJ"
      },
      "source": [
        "# 9. MACD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2Lg6aozUaDK"
      },
      "source": [
        "def MACD(df, fastEMA=12, slowEMA=26, signal=9, base='Close'):\n",
        "    \"\"\"\n",
        "    Function to compute Moving Average Convergence Divergence (MACD)\n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        fastEMA : Integer indicates faster EMA\n",
        "        slowEMA : Integer indicates slower EMA\n",
        "        signal : Integer indicates the signal generator for MACD\n",
        "        base : String indicating the column name from which the MACD needs to be computed from (Default Close)\n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new columns added for \n",
        "            Fast EMA (ema_$fastEMA)\n",
        "            Slow EMA (ema_$slowEMA)\n",
        "            MACD (macd_$fastEMA_$slowEMA_$signal)\n",
        "            MACD Signal (signal_$fastEMA_$slowEMA_$signal)\n",
        "            MACD Histogram (MACD (hist_$fastEMA_$slowEMA_$signal)) \n",
        "    \"\"\"\n",
        "    fE = \"ema_\" + str(fastEMA)\n",
        "    sE = \"ema_\" + str(slowEMA)\n",
        "    macd = \"macd_\" + str(fastEMA) + \"_\" + str(slowEMA) + \"_\" + str(signal)\n",
        "    sig = \"signal_\" + str(fastEMA) + \"_\" + str(slowEMA) + \"_\" + str(signal)\n",
        "    hist = \"hist_\" + str(fastEMA) + \"_\" + str(slowEMA) + \"_\" + str(signal)\n",
        "    # Compute fast and slow EMA    \n",
        "    EMA(df, base, fE, fastEMA)\n",
        "    EMA(df, base, sE, slowEMA)\n",
        "    # Compute MACD\n",
        "    df[macd] = np.where(np.logical_and(np.logical_not(df[fE] == 0), np.logical_not(df[sE] == 0)), df[fE] - df[sE], 0)\n",
        "    # Compute MACD Signal\n",
        "    EMA(df, macd, sig, signal)\n",
        "    # Compute MACD Histogram\n",
        "    df[hist] = np.where(np.logical_and(np.logical_not(df[macd] == 0), np.logical_not(df[sig] == 0)), df[macd] - df[sig], 0)\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThSc349_UaDP"
      },
      "source": [
        "# 10. BBand"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hueu64KSUaDQ"
      },
      "source": [
        "def BBand(df, base='Close', period=20, multiplier=2):\n",
        "    \"\"\"\n",
        "    Function to compute Bollinger Band (BBand)\n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        base : String indicating the column name from which the MACD needs to be computed from (Default Close)\n",
        "        period : Integer indicates the period of computation in terms of number of candles\n",
        "        multiplier : Integer indicates value to multiply the SD\n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new columns added for \n",
        "            Upper Band (UpperBB_$period_$multiplier)\n",
        "            Lower Band (LowerBB_$period_$multiplier)\n",
        "    \"\"\"\n",
        "    upper = 'UpperBB_' + str(period) + '_' + str(multiplier)\n",
        "    lower = 'LowerBB_' + str(period) + '_' + str(multiplier)\n",
        "    sma = df[base].rolling(window=period, min_periods=period - 1).mean()\n",
        "    sd = df[base].rolling(window=period).std()\n",
        "    df[upper] = sma + (multiplier * sd)\n",
        "    df[lower] = sma - (multiplier * sd)\n",
        "    df[upper].fillna(0, inplace=True)\n",
        "    df[lower].fillna(0, inplace=True)\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geRFfZVWUaDU"
      },
      "source": [
        "# 11. Ichimoku"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_oO6hTPUaDW"
      },
      "source": [
        "def Ichimoku(df, ohlc=['Open', 'High', 'Low', 'Close'], param=[9, 26, 52, 26]):\n",
        "    \"\"\"\n",
        "    Function to compute Ichimoku Cloud parameter (Ichimoku)    \n",
        "    Args :\n",
        "        df : Pandas DataFrame which contains ['date', 'open', 'high', 'low', 'close', 'volume'] columns\n",
        "        ohlc: List defining OHLC Column names (default ['Open', 'High', 'Low', 'Close'])\n",
        "        param: Periods to be used in computation (default [tenkan_sen_period, kijun_sen_period, senkou_span_period, chikou_span_period] = [9, 26, 52, 26])\n",
        "    Returns :\n",
        "        df : Pandas DataFrame with new columns added for ['Tenkan Sen', 'Kijun Sen', 'Senkou Span A', 'Senkou Span B', 'Chikou Span']\n",
        "    \"\"\"\n",
        "    high = df[ohlc[1]]\n",
        "    low = df[ohlc[2]]\n",
        "    close = df[ohlc[3]]\n",
        "    tenkan_sen_period = param[0]\n",
        "    kijun_sen_period = param[1]\n",
        "    senkou_span_period = param[2]\n",
        "    chikou_span_period = param[3]\n",
        "    tenkan_sen_column = 'Tenkan Sen'\n",
        "    kijun_sen_column = 'Kijun Sen'\n",
        "    senkou_span_a_column = 'Senkou Span A'\n",
        "    senkou_span_b_column = 'Senkou Span B'\n",
        "    chikou_span_column = 'Chikou Span'\n",
        "    # Tenkan-sen (Conversion Line)\n",
        "    tenkan_sen_high = high.rolling(window=tenkan_sen_period).max()\n",
        "    tenkan_sen_low = low.rolling(window=tenkan_sen_period).min()\n",
        "    df[tenkan_sen_column] = (tenkan_sen_high + tenkan_sen_low) / 2\n",
        "    # Kijun-sen (Base Line)\n",
        "    kijun_sen_high = high.rolling(window=kijun_sen_period).max()\n",
        "    kijun_sen_low = low.rolling(window=kijun_sen_period).min()\n",
        "    df[kijun_sen_column] = (kijun_sen_high + kijun_sen_low) / 2\n",
        "    # Senkou Span A (Leading Span A)\n",
        "    df[senkou_span_a_column] = ((df[tenkan_sen_column] + df[kijun_sen_column]) / 2).shift(kijun_sen_period)\n",
        "    # Senkou Span B (Leading Span B)\n",
        "    senkou_span_high = high.rolling(window=senkou_span_period).max()\n",
        "    senkou_span_low = low.rolling(window=senkou_span_period).min()\n",
        "    df[senkou_span_b_column] = ((senkou_span_high + senkou_span_low) / 2).shift(kijun_sen_period)\n",
        "    # The most current closing price plotted chikou_span_period time periods behind\n",
        "    df[chikou_span_column] = close.shift(-1 * chikou_span_period)\n",
        "    return df"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}